运动病
可靠性(半导体)
模拟
度量(数据仓库)
任务(项目管理)
运动(物理)
计算机科学
心理学
应用心理学
人工智能
工程类
数据挖掘
精神科
物理
功率(物理)
系统工程
量子力学
作者
Joseph Smyth,Stewart Birrell,Roger Woodman,Paul Jennings
标识
DOI:10.1016/j.apergo.2020.103315
摘要
Motion sickness (MS) is known to be a potentially limiting factor for future self-driving vehicles – specifically in regards to occupant comfort and well-being. With this as a consideration comes the desire to accurately measure, track and even predict MS state in real-time. Previous research has considered physiological measurements to measure MS state, although, this is mainly measured after an MS exposure and not throughout exposure(s) to a MS task. A unique contribution of this paper is in the real-time tracking of subjective MS alongside real-time physiological measurements of Electrodermal Activity (EDA) and skin temperature. Data was collected in both simulator-based (controlled) and on-road (naturalistic) studies. 40 participants provided at total of 61 data sets, providing 1603 min of motion sickness data for analysis. This study is in agreement that these measures are related to MS but evidenced a total lack of reliability for these measures at an individual level for both simulator and on-road experimentation. It is likely that other factors, such as environment and emotional state are more impactful on these physiological measures than MS itself. At a cohort level, the applicability of physiological measures is not considered useful for measuring MS accurately or reliably in real-time. Recommendations for further research include a mixed-measures approach to capture other data types (such as subject activity) and to remove contamination of physiological measures from environmental changes.
科研通智能强力驱动
Strongly Powered by AbleSci AI